Surbhi Goel

Surbhi Goel

Postdoc Researcher

Microsoft Research, New York City

I am a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group.

My research interests lie at the intersection of theoretical computer science and machine learning, with a focus on developing theoretical foundations for modern machine learning paradigms including deep learning. My work attempts to quantify the limitations of existing approaches and design new efficient algorithms with provable guarantees.

Prior to joining MSR, I obtained my Ph.D. in the Computer Science department at the University of Texas at Austin advised by Adam Klivans. My dissertation was awarded UTCS’s Bert Kay Dissertation award. My research was generously supported by the JP Morgan AI Fellowship and several fellowships from UT Austin. During my PhD, I visited IAS for the Theoretical Machine learning program and the Simons Institute for the Theory of Computing at UC Berkeley for the Foundations of Deep Learning program (supported by the Simons-Berkeley Research Fellowship). Before that, I received my Bachelors degree from Indian Institute of Technology (IIT) Delhi majoring in Computer Science and Engineering.

In Fall 2022/Spring 2023 (TBD), I will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania.

Download my resumé.

  • Theory
  • Machine Learning
  • PhD in Computer Science, 2020

    University of Texas at Austin

  • MS in Computer Science, 2019

    University of Texas at Austin

  • BTech in Computer Science and Engineering, 2015

    Indian Institute of Technology, Delhi

Recent Publications & Preprints

Understanding Contrastive Learning Requires Incorporating Inductive Biases
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Investigating the Role of Negatives in Contrastive Representation Learning
Inductive Biases and Variable Creation in Self-Attention Mechanisms
Gone Fishing: Neural Active Learning with Fisher Embeddings


Co-founded this community building and mentorship initiative for the learning theory community. Co-organized mentorship workshops at ALT 2021 and COLT 2021. Co-organized a graduate applications support program in collaboration with WiML-T.

Professional Services

Program Committee
Virtual Experience Chair
Co-organized the virtual part of the hybrid conference, including the 2-day virtual-only program.
Program Committee
Program Committee